This research paper focuses on the problem of dynamic objects and their impact on effective motion planning and localization. The paper proposes a two-step process to address this challenge, which involves finding the dynamic objects in the scene using a Flow-based method and then using a deep Video inpainting algorithm to remove them. The study aims to test the validity of this approach by comparing it with baseline results using two state-of-the-art SLAM algorithms, ORB-SLAM2 and LSD, and understanding the impact of dynamic objects and the corresponding trade-offs. The proposed approach does not require any significant modifications to the baseline SLAM algorithms, and therefore, the computational effort required remains unchanged. The paper presents a detailed analysis of the results obtained and concludes that the proposed method is effective in removing dynamic objects from the scene, leading to improved SLAM performance.

翻译：本研究聚焦动态目标对有效运动规划与定位所产生的影响问题。本文提出一个两阶段处理流程以应对该挑战：首先采用基于光流的方法检测场景中的动态目标，随后利用深度视频修复算法将其移除。本研究旨在通过对比两种主流SLAM算法（ORB-SLAM2与LSD）的基线实验结果，验证该方法的有效性，并深入分析动态目标的影响及其相应权衡。所提方法无需对基线SLAM算法进行显著修改，因此计算开销保持不变。本文对实验结果进行了详细分析，并得出结论：该方法能有效移除场景中的动态目标，从而提升SLAM性能。